IntelliFold: A Controllable Foundation Model for General and Specialized Biomolecular Structure Prediction
To quickly get started with IntelliFold, you can use the following commands:
# Install intellifold from PyPI
pip install intellifold
# Run inference with an example YAML file
intellifold predict ./examples/5S8I_A.yaml --out_dir ./output
To more complete installation instructions and usage, please refer to the Installation Guide.
-
Prepare Input File: Create a YAML file with your sequences following our input format specification
-
Run Prediction:
intellifold predict your_input.yaml --out_dir ./results
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Check Results: Find predicted structures and confidence scores in the output directory, you can also check the section of output format in output documentation.
-
Optional Optimization: Enable custom kernels for faster inference and reduced memory usage
For comprehensive usage instructions and examples, refer to the Usage Guide.
To comprehensively evaluate the performance of IntelliFold, we conducted a rigorous evaluation on FoldBench. We compared IntelliFold against several leading methods, including Boltz-1,2, Chai-1, Protenix and Alphafold3.
For more details on the benchmarking process and results, please refer to our Technical Report.
We highly recommend using the IntelliFold Server for the most accurate, complete, and convenient biomolecular structure predictions. It requires no installation and provides an intuitive web interface to submit your sequences and visualize results directly in your browser. The server runs the full, optimized, latest IntelliFold implementation for optimal performance.
If you use IntelliFold in your research, please cite our paper:
@misc{theintfoldteam2025intfoldcontrollablefoundationmodel,
title={IntFold: A Controllable Foundation Model for General and Specialized Biomolecular Structure Prediction},
author={The IntFold Team and Leon Qiao and Wayne Bai and He Yan and Gary Liu and Nova Xi and Xiang Zhang},
year={2025},
eprint={2507.02025},
archivePrefix={arXiv},
primaryClass={q-bio.BM},
url={https://arxiv.org/abs/2507.02025}
}
- The implementation of fast layernorm operators is inspired by OneFlow and FastFold, following Protenix's usage.
- Many components in
intellifold/openfold/
are adapted from OpenFold, with substantial modifications and improvements by our team (except for theLayerNorm
part). - This repository, the implementation of Inference Data Pipeline(Data/Feature Processing and MSA generation tasks) referred to Boltz-1, and modify some codes to adapt to the input of our model.
The IntelliFold project, including code and model parameters, is made available under the Apache 2.0 License, it is free for both academic research and commercial use.
If you have any questions or are interested in collaboration, please feel free to contact us at contact@intfold.com.